Exploratoy analyses on the eight phenotypic traits under study
Juliette Archambeau
18 août, 2021
# Options
ncol.wrap <- 7
x.wrap <- 0.85
y.wrap <- 0.09
height.wrap <- 10
width.wrap <- 14Traits selected in this study:
Height:
In Portugal (Fundão): height in October 2012 when the trees were 20-month old.
In Asturias(Cabada, Spain): height in November 2012 when the trees were 21-month old.
In Bordeaux (Pierroton, France): height in November 2013 when the trees were 25-month old & in November 2018 when the trees were 85-month old.
Phenology:
mean bud burst (=date of brachybalst emergence) over four years (2013, 2014, 2015 and 2017) in Bordeaux (°C-day)
mean duration of bud burst (in 2014)2014, 2015 and 2017) in Bordeaux (°C-day)
Functional traits
\(\delta^{13}C\)
Specific Leaf Area (SLA)
In the document, the plots colored in green correspond to the trait selected in the study.
# Load data
drivers <- readRDS(file="data/DF_Drivers.rds") %>%
dplyr::select(prov,A,D,EH.20km.PC1,EH.20km.PC2,EH.1km.PC1,EH.1km.PC2,SHM,EMT)
data <- readRDS(file="data/ClonapinData/PhenoDataNovember2019_AnnualTraits.rds") %>%
dplyr::filter(!(prov=="MAD")) %>%
inner_join(drivers,by="prov")1 Height
data %>% dplyr::select(contains("ht")) %>% summary## BDX_htnov13 BDX_htnov14 BDX_htnov15 BDX_htnov18 POR_htjan12 POR_htmay12 POR_htoct12 POR_htmay13 AST_htdec11 AST_htnov12 AST_htmar14
## Min. : 40.0 Min. : 50 Min. : 190 Min. : 530 Min. : 20.0 Min. : 20.0 Min. : 90.0 Min. : 150.0 Min. : 70.0 Min. : 90.0 Min. : 230
## 1st Qu.: 600.0 1st Qu.: 950 1st Qu.:1360 1st Qu.:3050 1st Qu.:155.0 1st Qu.:230.0 1st Qu.: 350.0 1st Qu.: 520.0 1st Qu.:230.0 1st Qu.: 580.0 1st Qu.:1020
## Median : 720.0 Median :1140 Median :1680 Median :3840 Median :210.0 Median :320.0 Median : 450.0 Median : 640.0 Median :280.0 Median : 700.0 Median :1250
## Mean : 737.8 Mean :1161 Mean :1712 Mean :3792 Mean :213.9 Mean :332.9 Mean : 463.1 Mean : 646.8 Mean :288.9 Mean : 710.7 Mean :1252
## 3rd Qu.: 860.0 3rd Qu.:1350 3rd Qu.:2040 3rd Qu.:4550 3rd Qu.:265.0 3rd Qu.:420.0 3rd Qu.: 560.0 3rd Qu.: 760.0 3rd Qu.:340.0 3rd Qu.: 840.0 3rd Qu.:1470
## Max. :1650.0 Max. :2550 Max. :3770 Max. :7660 Max. :580.0 Max. :870.0 Max. :1150.0 Max. :1450.0 Max. :700.0 Max. :1640.0 Max. :2670
## NA's :8448 NA's :8449 NA's :8468 NA's :8477 NA's :7542 NA's :7920 NA's :8940 NA's :9027 NA's :7725 NA's :7713 NA's :77181.1 Bordeaux (France) - November 2013 and 2018
1.1.1 All height values
plot_grid(
ggplot(data, aes(x= BDX_htnov13)) +
geom_histogram(binwidth = 20, fill="seagreen3") +
theme_bw() +
labs(x="Height 2013",y="") +
annotate("text",x=1500,y=150,label=paste0("Var = ",round(var(data$BDX_htnov13,na.rm=T),2))),
ggplot(data, aes(x= BDX_htnov14)) +
geom_histogram(binwidth = 20) +
theme_bw() +
labs(x="Height 2014",y="") +
annotate("text",x=2300,y=80,label=paste0("Var = ",round(var(data$BDX_htnov14,na.rm=T),2))),
ggplot(data, aes(x= BDX_htnov15)) +
geom_histogram(binwidth = 20) +
theme_bw() +
labs(x="Height 2015",y="") +
annotate("text",x=3000,y=60,label=paste0("Var = ",round(var(data$BDX_htnov15,na.rm=T),2))),
ggplot(data, aes(x= BDX_htnov18)) +
geom_histogram(binwidth = 20, fill="seagreen3") +
theme_bw() +
labs(x="Height 2018",y="") +
annotate("text",x=6500,y=50,label=paste0("Var = ",round(var(data$BDX_htnov18,na.rm=T),2))))
1.1.2 Height values per provenances
1.1.2.1 Height - 25-month old - November 2013
# Per provenance
# --------------
# Clones are colored according to the main gene pool (the gene pool for which the provenances have the highest proportion of belonging)
p <- ggplot(data, aes(x=BDX_htnov13, fill=as.factor(max.Q))) +
geom_histogram(alpha=0.7) +
theme_bw() +
facet_wrap(~as.factor(prov)) +
scale_fill_manual(values=c("orangered3", # Northern Africa
"gold2", # Corsica
"darkorchid3", # Central Spain
"navyblue", # French Atlantic
"turquoise2", # Iberian Atlantic
"green3" # South-eastern Spain
),
labels=c("Northern Africa",
"Corsica",
"Central Spain",
"French Atlantic",
"Iberian Atlantic",
"South-eastern Spain")
,name="Gene pools") +
labs(x="",y="") +
theme(legend.position = c(0.75, 0.07),
legend.text = element_text(size=12),
legend.title = element_text(size=14)) +
guides(fill=guide_legend(nrow=2))
p
ggsave(p,file="figs/ExploratoryAnalyses/HeightBordeaux2013.png",height=10,width=12)# Per provenance
# --------------
# Provenances are colored according to the value of EMT in their location
p <- ggplot(data, aes(x=BDX_htnov13, fill=EMT)) +
geom_histogram(alpha=0.7) +
theme_bw() +
facet_wrap(~as.factor(prov), ncol = ncol.wrap) +
labs(x="",y="") +
scale_fill_gradient2(midpoint = mean(data$EMT,na.rm=T),
low = "blue",
mid = "yellow",
high = "red",
space = "Lab" ,
name="Extreme minimum temperature") +
theme(legend.position = c(x.wrap,y.wrap),
legend.text = element_text(size=10),
legend.title = element_text(size=12))
p
ggsave(p,file="figs/ExploratoryAnalyses/HeightBordeaux2013_EMTgradient.png",height=height.wrap,width=width.wrap)1.1.2.2 Height - 85-month old - November 2018
# Per provenance
# --------------
# Clones are colored according to the main gene pool (the gene pool for which the provenances have the highest proportion of belonging)
p <- ggplot(data, aes(x=BDX_htnov18, fill=as.factor(max.Q))) +
geom_histogram(alpha=0.7) +
theme_bw() +
facet_wrap(~as.factor(prov)) +
scale_fill_manual(values=c("orangered3", # Nprthern Africa
"gold2",# Corsica
"darkorchid3", # Central Spain
"navyblue", # french Atlantic
"turquoise2", # Iberian Atlantic
"green3" # South-eastern Spain
),
labels=c("Northern Africa",
"Corsica",
"Central Spain",
"French Atlantic",
"Iberian Atlantic",
"South-eastern Spain")
,name="Gene pools") +
labs(x="",y="") +
theme(legend.position = c(0.75, 0.07),
legend.text = element_text(size=12),
legend.title = element_text(size=14)) +
guides(fill=guide_legend(nrow=2))
p
ggsave(p,file="figs/ExploratoryAnalyses/HeightBordeaux2018.png",height=10,width=12)# Per provenance
# --------------
# Provenances are colored according to the value of EMT in their location
p <- ggplot(data, aes(x=BDX_htnov18, fill=EMT)) +
geom_histogram(alpha=0.7) +
theme_bw() +
facet_wrap(~as.factor(prov), ncol = ncol.wrap) +
labs(x="",y="") +
scale_fill_gradient2(midpoint = mean(data$EMT,na.rm=T),
low = "blue",
mid = "yellow",
high = "red",
space = "Lab" ,
name="Extreme minimum temperature") +
theme(legend.position = c(x.wrap,y.wrap),
legend.text = element_text(size=10),
legend.title = element_text(size=12))
p
ggsave(p,file="figs/ExploratoryAnalyses/HeightBordeaux2018_EMTgradient.png",height=height.wrap,width=width.wrap)1.2 Cabada (Asturias, Spain) - November 2012
1.2.1 All height values
plot_grid(
ggplot(data, aes(x= AST_htdec11)) +
geom_histogram(binwidth = 20) +
theme_bw() +
labs(x="Height - December 2011",y="") +
annotate("text",x=580,y=300,label=paste0("Var = ",round(var(data$BDX_htnov13,na.rm=T),2))),
ggplot(data, aes(x= AST_htnov12)) +
geom_histogram(binwidth = 20, fill="seagreen3") +
theme_bw() +
labs(x="Height - November 2012",y="") +
annotate("text",x=1200,y=150,label=paste0("Var = ",round(var(data$BDX_htnov14,na.rm=T),2))),
ggplot(data, aes(x= AST_htmar14)) +
geom_histogram(binwidth = 20) +
theme_bw() +
labs(x="Height - March 2014",y="") +
annotate("text",x=2100,y=100,label=paste0("Var = ",round(var(data$BDX_htnov15,na.rm=T),2))))
1.2.2 Height values per provenances
# Per provenance
# --------------
# Clones are colored according to the main gene pool (the gene pool for which the provenances have the highest proportion of belonging)
p <- ggplot(data, aes(x=AST_htnov12, fill=as.factor(max.Q))) +
geom_histogram(alpha=0.7) +
theme_bw() +
facet_wrap(~as.factor(prov)) +
scale_fill_manual(values=c("orangered3", # Northern Africa
"gold2", # Corsica
"darkorchid3", # Central Spain
"navyblue", # French Atlantic
"turquoise2", # Iberian Atlantic
"green3" # South-eastern Spain
),
labels=c("Northern Africa",
"Corsica",
"Central Spain",
"French Atlantic",
"Iberian Atlantic",
"South-eastern Spain")
,name="Gene pools") +
labs(x="",y="") +
theme(legend.position = c(0.75, 0.07),
legend.text = element_text(size=12),
legend.title = element_text(size=14)) +
guides(fill=guide_legend(nrow=2))
p
ggsave(p,file="figs/ExploratoryAnalyses/HeightAsturias2012.png",height=10,width=12)# Per provenance
# --------------
# Provenances are colored according to the value of EMT in their location
p <- ggplot(data, aes(x=AST_htnov12, fill=EMT)) +
geom_histogram(alpha=0.7) +
theme_bw() +
facet_wrap(~as.factor(prov), ncol = ncol.wrap) +
labs(x="",y="") +
scale_fill_gradient2(midpoint = mean(data$EMT,na.rm=T),
low = "blue",
mid = "yellow",
high = "red",
space = "Lab" ,
name="Extreme minimum temperature") +
theme(legend.position = c(x.wrap,y.wrap),
legend.text = element_text(size=10),
legend.title = element_text(size=12))
p
ggsave(p,file="figs/ExploratoryAnalyses/HeightAsturias2012_EMTgradient.png",height=height.wrap,width=width.wrap)1.3 Portugal (Fundão) - October 2012
1.3.1 All height values
plot_grid(
ggplot(data, aes(x= POR_htjan12)) +
geom_histogram(binwidth = 20) +
theme_bw() +
labs(x="Height - January 2012",y="") +
annotate("text",x=400,y=300,label=paste0("Var = ",round(var(data$POR_htjan12,na.rm=T),2))),
ggplot(data, aes(x= POR_htmay12)) +
geom_histogram(binwidth = 20) +
theme_bw() +
labs(x="Height - May 2012",y="") +
annotate("text",x=600,y=150,label=paste0("Var = ",round(var(data$POR_htmay12,na.rm=T),2))),
ggplot(data, aes(x= POR_htoct12)) +
geom_histogram(binwidth = 20,fill="seagreen3") +
theme_bw() +
labs(x="Height - October 2012",y="") +
annotate("text",x=900,y=100,label=paste0("Var = ",round(var(data$POR_htoct12,na.rm=T),2))),
ggplot(data, aes(x= POR_htmay13)) +
geom_histogram(binwidth = 20) +
theme_bw() +
labs(x="Height - May 2013",y="") +
annotate("text",x=1200,y=100,label=paste0("Var = ",round(var(data$POR_htmay13,na.rm=T),2))))
1.3.2 Height values per provenances
# Per provenance
# --------------
# Clones are colored according to the main gene pool (the gene pool for which the provenances have the highest proportion of belonging)
p <- ggplot(data, aes(x=POR_htoct12, fill=as.factor(max.Q))) +
geom_histogram(alpha=0.7) +
theme_bw() +
facet_wrap(~as.factor(prov)) +
scale_fill_manual(values=c("orangered3", # Northern Africa
"gold2", # Corsica
"darkorchid3", # Central Spain
"navyblue", # French Atlantic
"turquoise2", # Iberian Atlantic
"green3" # South-eastern Spain
),
labels=c("Northern Africa",
"Corsica",
"Central Spain",
"French Atlantic",
"Iberian Atlantic",
"South-eastern Spain")
,name="Gene pools") +
labs(x="",y="") +
theme(legend.position = c(0.75, 0.07),
legend.text = element_text(size=12),
legend.title = element_text(size=14)) +
guides(fill=guide_legend(nrow=2))
p
ggsave(p,file="figs/ExploratoryAnalyses/HeightPortugal2012.png",height=10,width=12)# Per provenance
# --------------
# Provenances are colored according to the value of EMT in their location
p <- ggplot(data, aes(x=POR_htoct12, fill=EMT)) +
geom_histogram(alpha=0.7) +
theme_bw() +
facet_wrap(~as.factor(prov), ncol = ncol.wrap) +
labs(x="",y="") +
scale_fill_gradient2(midpoint = mean(data$EMT,na.rm=T),
low = "blue",
mid = "yellow",
high = "red",
space = "Lab" ,
name="Extreme minimum temperature") +
theme(legend.position = c(x.wrap,y.wrap),
legend.text = element_text(size=10),
legend.title = element_text(size=12))
p
ggsave(p,file="figs/ExploratoryAnalyses/HeightPortugal2012_EMTgradient.png",height=height.wrap,width=width.wrap)# Per block
# --------------
data %>%
drop_na(POR_htoct12) %>%
droplevels() %>%
ggplot(aes(x=POR_htoct12, fill=as.factor(block))) +
geom_histogram(alpha=0.7) +
theme_bw() +
facet_wrap(~as.factor(block)) +
labs(x="",y="") +
theme(legend.position = "none")
2 Phenology-related traits
2.1 Bud burst
2.1.1 All values
data %>% dplyr::select(contains("s3")) %>% summary## BDX_s32013 BDX_s32014 BDX_s32015 BDX_s32017
## Min. : 923 Min. :1212 Min. :1067 Min. :1011
## 1st Qu.:1031 1st Qu.:1374 1st Qu.:1305 1st Qu.:1320
## Median :1195 Median :1485 Median :1305 Median :1320
## Mean :1105 Mean :1449 Mean :1313 Mean :1287
## 3rd Qu.:1195 3rd Qu.:1485 3rd Qu.:1305 3rd Qu.:1320
## Max. :1305 Max. :1912 Max. :1915 Max. :1570
## NA's :8708 NA's :8623 NA's :8578 NA's :10287There is less missing values in 2015.
plot_grid(
ggplot(data, aes(x= BDX_s32013)) +
geom_histogram(binwidth = 20) +
theme_bw() +
labs(x="Bud burst 2013",y="") +
annotate("text",x=1000,y=900,label=paste0("Var = ",round(var(data$BDX_s32013,na.rm=T),2))),
ggplot(data, aes(x= BDX_s32014)) +
geom_histogram(binwidth = 20,fill="seagreen3") +
theme_bw() +
labs(x="Bud burst 2014",y="") +
annotate("text",x=1700,y=750,label=paste0("Var = ",round(var(data$BDX_s32014,na.rm=T),2))),
ggplot(data, aes(x= BDX_s32015)) +
geom_histogram(binwidth = 20) +
theme_bw() +
labs(x="Bud burst 2015",y="") +
annotate("text",x=1500,y=1500,label=paste0("Var = ",round(var(data$BDX_s32015,na.rm=T),2))),
ggplot(data, aes(x= BDX_s32017)) +
geom_histogram(binwidth = 20) +
theme_bw() +
labs(x="Bud burst 2017",y="") +
annotate("text",x=1200,y=900,label=paste0("Var = ",round(var(data$BDX_s32017,na.rm=T),2))))
meanBB <-data %>%
dplyr::select(prov,clon,tree,block,BDX_s32013,BDX_s32014,BDX_s32015,BDX_s32017) %>%
filter(rowSums(is.na(across(where(is.numeric)))) != ncol(.)-4) %>%
dplyr::mutate(mean = rowMeans(select(.,contains("BDX")),na.rm=T))
meanBB %>%
ggplot(aes(x= mean)) +
geom_histogram(binwidth = 15) +
theme_bw() +
labs(x="Mean bud burst over the 4 measurement years",y="")
2.1.2 Per provenance
# Per provenance
# --------------
# Clones are colored according to the main gene pool (the gene pool for which the provenances have the highest proportion of belonging)
p <- ggplot(data, aes(x=BDX_s32014, fill=as.factor(max.Q))) +
geom_histogram(alpha=0.7) +
theme_bw() +
facet_wrap(~as.factor(prov)) +
scale_fill_manual(values=c("orangered3", # Northern Africa
"gold2", # Corsica
"darkorchid3", # Central Spain
"navyblue", # French Atlantic
"turquoise2", # Iberian Atlantic
"green3" # South-eastern Spain
),
labels=c("Northern Africa",
"Corsica",
"Central Spain",
"French Atlantic",
"Iberian Atlantic",
"South-eastern Spain")
,name="Gene pools") +
labs(x="",y="") +
theme(legend.position = c(0.75, 0.07),
legend.text = element_text(size=12),
legend.title = element_text(size=14)) +
guides(fill=guide_legend(nrow=2))
p
ggsave(p,file="figs/ExploratoryAnalyses/BudBurstBordeaux2014.png",height=10,width=12)# Per provenance
# --------------
# Provenances are colored according to the value of EMT in their location
p <- ggplot(data, aes(x=BDX_s32014, fill=EMT)) +
geom_histogram(alpha=0.7) +
theme_bw() +
facet_wrap(~as.factor(prov), ncol = ncol.wrap) +
labs(x="",y="") +
scale_fill_gradient2(midpoint = mean(data$EMT,na.rm=T),
low = "blue",
mid = "yellow",
high = "red",
space = "Lab" ,
name="Extreme minimum temperature") +
theme(legend.position = c(x.wrap,y.wrap),
legend.text = element_text(size=10),
legend.title = element_text(size=12))
p
ggsave(p,file="figs/ExploratoryAnalyses/BudBurstBordeaux2014_EMTgradient.png",height=height.wrap,width=width.wrap)2.2 Duration of bud burst
2.2.1 All values
data %>% dplyr::select(contains("dbb")) %>% summary## BDX_dbb2014 BDX_dbb2015 BDX_dbb2017 BDX_dbb2013
## Min. : 358.0 Min. : 443.0 Min. : 478.5 Min. :382
## 1st Qu.: 766.0 1st Qu.: 717.0 1st Qu.: 811.5 1st Qu.:382
## Median : 847.0 Median : 824.0 Median : 890.3 Median :382
## Mean : 869.7 Mean : 815.5 Mean : 902.2 Mean :382
## 3rd Qu.:1079.0 3rd Qu.: 947.0 3rd Qu.: 979.2 3rd Qu.:382
## Max. :1372.0 Max. :1219.0 Max. :1121.9 Max. :382
## NA's :8731 NA's :8572 NA's :9836 NA's :11657There is less missing values in 2015.
plot_grid(
ggplot(data, aes(x= BDX_dbb2013)) +
geom_histogram(binwidth = 20) +
theme_bw() +
labs(x="Duration bud burst 2013",y="") +
annotate("text",x=400,y=500,label=paste0("Var = ",round(var(data$BDX_dbb2013,na.rm=T),2))),
ggplot(data, aes(x= BDX_dbb2014)) +
geom_histogram(binwidth = 20,fill="seagreen3") +
theme_bw() +
labs(x="Duration bud burst 2014",y="") +
annotate("text",x=750,y=800,label=paste0("Var = ",round(var(data$BDX_dbb2014,na.rm=T),2))),
ggplot(data, aes(x= BDX_dbb2015)) +
geom_histogram(binwidth = 20) +
theme_bw() +
labs(x="Duration bud burst 2015",y="") +
annotate("text",x=900,y=1000,label=paste0("Var = ",round(var(data$BDX_dbb2015,na.rm=T),2))),
ggplot(data, aes(x= BDX_dbb2017)) +
geom_histogram(binwidth = 20) +
theme_bw() +
labs(x="Duration bud burst 2017",y="") +
annotate("text",x=800,y=750,label=paste0("Var = ",round(var(data$BDX_dbb2017,na.rm=T),2))))
meanDBB <- data %>%
dplyr::select(prov,clon,tree,block,BDX_dbb2014,BDX_dbb2015,BDX_dbb2017) %>%
dplyr::filter(rowSums(is.na(across(where(is.numeric)))) != ncol(.)-4) %>%
dplyr::mutate(mean = rowMeans(select(.,contains("BDX")),na.rm=T))
meanDBB %>% ggplot(aes(x= mean)) +
geom_histogram(binwidth = 15) +
theme_bw() +
labs(x="Mean duration of bud burst over the 3 measurement years",y="")
2.2.2 Per provenance
# Per provenance
# --------------
# Clones are colored according to the main gene pool (the gene pool for which the provenances have the highest proportion of belonging)
p <- ggplot(data, aes(x=BDX_dbb2014, fill=as.factor(max.Q))) +
geom_histogram(alpha=0.7) +
theme_bw() +
facet_wrap(~as.factor(prov)) +
scale_fill_manual(values=c("orangered3", # Northern Africa
"gold2", # Corsica
"darkorchid3", # Central Spain
"navyblue", # French Atlantic
"turquoise2", # Iberian Atlantic
"green3" # South-eastern Spain
),
labels=c("Northern Africa",
"Corsica",
"Central Spain",
"French Atlantic",
"Iberian Atlantic",
"South-eastern Spain")
,name="Gene pools") +
labs(x="",y="") +
theme(legend.position = c(0.75, 0.07),
legend.text = element_text(size=12),
legend.title = element_text(size=14)) +
guides(fill=guide_legend(nrow=2))
p
ggsave(p,file="figs/ExploratoryAnalyses/DurationBudBurstBordeaux2014.png",height=10,width=12)# Per provenance
# --------------
# Provenances are colored according to the value of EMT in their location
p <- ggplot(data, aes(x=BDX_dbb2014, fill=EMT)) +
geom_histogram(alpha=0.7) +
theme_bw() +
facet_wrap(~as.factor(prov), ncol = ncol.wrap) +
labs(x="",y="") +
scale_fill_gradient2(midpoint = mean(data$EMT,na.rm=T),
low = "blue",
mid = "yellow",
high = "red",
space = "Lab" ,
name="Extreme minimum temperature") +
theme(legend.position = c(x.wrap,y.wrap),
legend.text = element_text(size=10),
legend.title = element_text(size=12))
p
ggsave(p,file="figs/ExploratoryAnalyses/DurationBudBurstBordeaux2014_EMTgradient.png",
height=height.wrap,width=width.wrap)table(data$BDX_s12013,data$BDX_s42013) # In 2013, only 32 trees were both measured at stage s1 and s4.##
## 923 1031 1195 1230 1305
## 923 0 0 0 0 29
## 1031 0 0 0 0 0
## 1195 0 0 0 0 0
## 1230 0 0 0 0 0
## 1305 0 0 0 0 0table(data$BDX_s12014,data$BDX_s42014)##
## 1212 1277 1374 1485 1599 1680 1912 2205
## 833 1 4 52 314 567 670 848 5
## 919 0 1 4 20 61 118 204 3
## 1031 0 0 0 2 11 19 27 1
## 1212 0 0 0 0 0 5 16 2table(data$BDX_s12015,data$BDX_s42015)##
## 1305 1413 1520 1644 1785 1915
## 696 141 1146 839 820 100 12
## 862 2 12 14 16 11 1table(data$BDX_s12017,data$BDX_s42017)##
## 1319.7 1570 1712.7
## 590.8 1 828 1
## 679.7 0 402 2
## 758.5 0 586 3
## 840.5 0 10 1
## 932.5 0 13 0
## 1091.5 0 2 1In the following analyses, we are going to select the mean bud burst date over the four years and the mean duration of bud burst over three years, as it will be easier from a statistical point of view (the trait distribution is almost normal), and it buffers the variations among years.
3 Functional traits
3.1 \(\delta^{13}C\)
data %>% dplyr::select(contains("d13C")) %>% summary## d13C
## Min. :-30.47
## 1st Qu.:-27.14
## Median :-26.18
## Mean :-26.26
## 3rd Qu.:-25.39
## Max. :-22.59
## NA's :97473.1.1 All values
ggplot(data, aes(x= d13C)) +
geom_histogram(binwidth = 0.1,fill="seagreen3") +
theme_bw() +
labs(x="Delta 13 C",y="") +
annotate("text",x=-30,y=60,label=paste0("Var = ",round(var(data$d13C,na.rm=T),2)))
3.1.2 Per provenance
# Per provenance
# --------------
# Clones are colored according to the main gene pool (the gene pool for which the provenances have the highest proportion of belonging)
p <- ggplot(data, aes(x=d13C, fill=as.factor(max.Q))) +
geom_histogram(alpha=0.7) +
theme_bw() +
facet_wrap(~as.factor(prov)) +
scale_fill_manual(values=c("orangered3", # Northern Africa
"gold2", # Corsica
"darkorchid3", # Central Spain
"navyblue", # French Atlantic
"turquoise2", # Iberian Atlantic
"green3" # South-eastern Spain
),
labels=c("Northern Africa",
"Corsica",
"Central Spain",
"French Atlantic",
"Iberian Atlantic",
"South-eastern Spain")
,name="Gene pools") +
labs(x="",y="") +
theme(legend.position = c(0.75, 0.07),
legend.text = element_text(size=12),
legend.title = element_text(size=14)) +
guides(fill=guide_legend(nrow=2))
p
ggsave(p,file="figs/ExploratoryAnalyses/d13C.png",height=10,width=12)# Per provenance
# --------------
# Provenances are colored according to the value of EMT in their location
p <- ggplot(data, aes(x=d13C, fill=EMT)) +
geom_histogram(alpha=0.7) +
theme_bw() +
facet_wrap(~as.factor(prov), ncol = ncol.wrap) +
labs(x="",y="") +
scale_fill_gradient2(midpoint = mean(data$EMT,na.rm=T),
low = "blue",
mid = "yellow",
high = "red",
space = "Lab" ,
name="Extreme minimum temperature") +
theme(legend.position = c(x.wrap,y.wrap),
legend.text = element_text(size=10),
legend.title = element_text(size=12))
p
ggsave(p,file="figs/ExploratoryAnalyses/d13C_EMTgradient.png",height=height.wrap,width=width.wrap)3.2 Specific leaf area
data %>% dplyr::select(contains("SLA")) %>% summary## POR_SLA
## Min. :2.253
## 1st Qu.:4.620
## Median :5.157
## Mean :5.258
## 3rd Qu.:5.790
## Max. :9.534
## NA's :90443.2.1 All values
plot_grid(ggplot(data, aes(x= POR_SLA)) +
geom_histogram(binwidth = 0.1,fill="seagreen3") +
theme_bw() +
labs(x="Specific Leaf Area",y="") +
annotate("text",x=9,y=130,label=paste0("Var = ",round(var(data$POR_SLA,na.rm=T),2))),
ggplot(data, aes(x= log(POR_SLA))) +
geom_histogram(binwidth = 0.01,fill="seagreen3") +
theme_bw() +
labs(x="Specific Leaf Area",y="") +
annotate("text",x=2.05,y=50,label=paste0("Var = ",round(var(log(data$POR_SLA),na.rm=T),2))),
nrow=1)
3.2.2 Per provenance
# Per provenance
# --------------
# Clones are colored according to the main gene pool (the gene pool for which the provenances have the highest proportion of belonging)
p <- ggplot(data, aes(x=POR_SLA, fill=as.factor(max.Q))) +
geom_histogram(alpha=0.7) +
theme_bw() +
facet_wrap(~as.factor(prov)) +
scale_fill_manual(values=c("orangered3", # Northern Africa
"gold2", # Corsica
"darkorchid3", # Central Spain
"navyblue", # French Atlantic
"turquoise2", # Iberian Atlantic
"green3" # South-eastern Spain
),
labels=c("Northern Africa",
"Corsica",
"Central Spain",
"French Atlantic",
"Iberian Atlantic",
"South-eastern Spain")
,name="Gene pools") +
labs(x="",y="") +
theme(legend.position = c(0.75, 0.07),
legend.text = element_text(size=12),
legend.title = element_text(size=14)) +
guides(fill=guide_legend(nrow=2))
p
ggsave(p,file="figs/ExploratoryAnalyses/PORSLA.png",height=10,width=12)# Per provenance
# --------------
# Provenances are colored according to the value of EMT in their location
p <- ggplot(data, aes(x=POR_SLA, fill=EMT)) +
geom_histogram(alpha=0.7) +
theme_bw() +
facet_wrap(~as.factor(prov), ncol = ncol.wrap) +
labs(x="",y="") +
scale_fill_gradient2(midpoint = mean(data$EMT,na.rm=T),
low = "blue",
mid = "yellow",
high = "red",
space = "Lab" ,
name="Extreme minimum temperature") +
theme(legend.position = c(x.wrap,y.wrap),
legend.text = element_text(size=10),
legend.title = element_text(size=12))
p
ggsave(p,file="figs/ExploratoryAnalyses/PORSLA_EMTgradient.png",height=height.wrap,width=width.wrap)4 Summary - All selected traits
4.1 Figure S1
fontsize=8
ymean = 0.82
yvar = 0.92
p <- plot_grid(
# Height Portugal (Fundão) 2012
ggplot(data, aes(x= POR_htoct12)) +
geom_histogram(binwidth = 20,fill="#B2182B",alpha=0.7) +
theme_bw() +
labs(x="Height (Portugal, 20 months, mm)",y="") +
annotation_custom(grobTree(textGrob(paste0("Variance : ",
round(var(data$POR_htoct12,na.rm=T),2)),
x=0.7,
y=yvar,
hjust=0,
gp=gpar(fontsize=fontsize)))) +
annotation_custom(grobTree(textGrob(paste0("Mean : ",
round(mean(data$POR_htoct12,na.rm=T),2)),
x=0.7,
y=ymean,
hjust=0,
gp=gpar(fontsize=fontsize)))),
# Height Bordeaux 2013
ggplot(data, aes(x= BDX_htnov13)) +
geom_histogram(binwidth = 20, fill="#D73027",alpha=0.7) +
theme_bw() +
labs(x="Height (Bordeaux, 25 months, mm)",y="") +
annotation_custom(grobTree(textGrob(paste0("Variance : ",
round(var(data$BDX_htnov13,na.rm=T),2)),
x=0.7,
y=yvar,
hjust=0,
gp=gpar(fontsize=fontsize)))) +
annotation_custom(grobTree(textGrob(paste0("Mean : ",
round(mean(data$BDX_htnov13,na.rm=T),2)),
x=0.7,
y=ymean,
hjust=0,
gp=gpar(fontsize=fontsize)))),
# Height Bordeaux 2018
ggplot(data, aes(x= BDX_htnov18)) +
geom_histogram(binwidth = 20, fill="#F46D43",alpha=0.7) +
theme_bw() +
labs(x="Height (Bordeaux, 85 months, mm)",y="") +
annotation_custom(grobTree(textGrob(paste0("Variance : ",
round(var(data$BDX_htnov18,na.rm=T),2)),
x=0.7,
y=yvar,
hjust=0,
gp=gpar(fontsize=fontsize)))) +
annotation_custom(grobTree(textGrob(paste0("Mean : ",
round(mean(data$BDX_htnov18,na.rm=T),2)),
x=0.7,
y=ymean,
hjust=0,
gp=gpar(fontsize=fontsize)))),
# Height Cabada (Asturias) 2012
ggplot(data, aes(x= AST_htnov12)) +
geom_histogram(binwidth = 20, fill="#FDAE61",alpha=0.7) +
theme_bw() +
labs(x="Height (Asturias, 21 months, mm)",y="") +
annotation_custom(grobTree(textGrob(paste0("Variance : ",
round(var(data$AST_htnov12,na.rm=T),2)),
x=0.7,
y=yvar,
hjust=0,
gp=gpar(fontsize=fontsize)))) +
annotation_custom(grobTree(textGrob(paste0("Mean : ",
round(mean(data$AST_htnov12,na.rm=T),2)),
x=0.7,
y=ymean,
hjust=0,
gp=gpar(fontsize=fontsize)))),
# Mean bud burst date Bordeaux
ggplot(meanBB,aes(x= mean)) +
geom_histogram(binwidth = 10,fill="#5AAE61",alpha=0.7) +
theme_bw() +
labs(x="Mean bud burst date (Bordeaux, over four years, °C-day)",y="") +
annotation_custom(grobTree(textGrob(paste0("Variance : ",
round(var(meanBB$mean,na.rm=T),2)),
x=0.7,
y=yvar,
hjust=0,
gp=gpar(fontsize=fontsize)))) +
annotation_custom(grobTree(textGrob(paste0("Mean : ",
round(mean(meanBB$mean,na.rm=T),2)),
x=0.7,
y=ymean,
hjust=0,
gp=gpar(fontsize=fontsize)))),
# Mean duration bud burst Bordeaux
ggplot(meanDBB,aes(x= mean)) +
geom_histogram(binwidth = 10,fill="#A6DBA0",alpha=0.7) +
theme_bw() +
labs(x="Mean duration of bud burst (Bordeaux, over three years, °C-day)",y="") +
annotation_custom(grobTree(textGrob(paste0("Variance : ",
round(var(meanDBB$mean,na.rm=T),2)),
x=0.7,
y=yvar,
hjust=0,
gp=gpar(fontsize=fontsize)))) +
annotation_custom(grobTree(textGrob(paste0("Mean : ",
round(mean(meanDBB$mean,na.rm=T),2)),
x=0.7,
y=ymean,
hjust=0,
gp=gpar(fontsize=fontsize)))),
# SLA Portugal
ggplot(data, aes(x= log(POR_SLA))) +
geom_histogram(binwidth = 0.01,fill="#4575B4",alpha=0.7) +
theme_bw() +
labs(x=TeX("Log(Specific Leaf Area) (Portugal, m$^{2}$/kg)"),y="") +
annotation_custom(grobTree(textGrob(paste0("Variance : ",
round(var(log(data$POR_SLA),na.rm=T),2)),
x=0.7,
y=yvar,
hjust=0,
gp=gpar(fontsize=fontsize)))) +
annotation_custom(grobTree(textGrob(paste0("Mean : ",
round(mean(log(data$POR_SLA),na.rm=T),2)),
x=0.7,
y=ymean,
hjust=0,
gp=gpar(fontsize=fontsize)))),
# delta13C Portugal
ggplot(data, aes(x= d13C)) +
geom_histogram(binwidth = 0.1,fill="#ABD9E9",alpha=0.7) +
theme_bw() +
labs(x=TeX("$\\delta^{13}C$ (Portugal, ‰)"),y="")+
annotation_custom(grobTree(textGrob(paste0("Variance : ",
round(var(data$d13C,na.rm=T),2)),
x=0.7,
y=yvar,
hjust=0,
gp=gpar(fontsize=fontsize)))) +
annotation_custom(grobTree(textGrob(paste0("Mean : ",
round(mean(data$d13C,na.rm=T),2)),
x=0.7,
y=ymean,
hjust=0,
gp=gpar(fontsize=fontsize)))),
nrow=4)
p
ggsave(p,file=paste0("figs/ExploratoryAnalyses/TraitsDistribution.png"),width = 10,height=8)4.2 Table S1 & S2
# the numbers in the columns "Trees", "Populations" and "Clones" were checked with the model inputs (May 12th)
tibble(Traits=c(rep("Height",4),"Mean bud burst date", "Mean duration of bud burst","Surface Leaf Area","$\\delta^{13}$C"),
"Common gardens"=c("Portugal",rep("Bordeaux",2),"Cabada",rep("Bordeaux",2),rep("Portugal",2)),
"Dates of measurement" =c("October 2012","November 2013", "November 2018","November 2012", "2013, 2014, 2015, 2017", "2014, 2015 2017", NA,NA),
"Tree age"=c(20,25,85,21,"-","-",NA,NA),
"Survival"=c(
# Proportion survival Portugal
round(table(data$POR_survoct12)[[2]]/(table(data$POR_survoct12)[[1]]+table(data$POR_survoct12)[[2]]),2),
# Proportion survival Bordeaux 2013
round(table(data$BDX_surv13)[[2]]/(table(data$BDX_surv13)[[1]]+table(data$BDX_surv13)[[2]]),2),
# Proportion survival Bordeaux 2018
round(table(data$BDX_surv18)[[2]]/(table(data$BDX_surv18)[[1]]+table(data$BDX_surv18)[[2]]),2),
# Proportion survival Asturias
round(table(data$AST_survnov12)[[2]]/(table(data$AST_survnov12)[[1]]+table(data$AST_survnov12)[[2]]),2),
"-","-","-","-"),
"Units"=c(rep("mm",4),"°C-day","°C-day","m$^{2}$/kg","‰"),
"Trees"=c(length(data$tree[!is.na(data$POR_htoct12)]), # height Portugal 2012
length(data$tree[!is.na(data$BDX_htnov13)]), # height Bordeaux 2013
length(data$tree[!is.na(data$BDX_htnov18)]), # height Bordeaux 2018
length(data$tree[!is.na(data$AST_htnov12)]), # height Cabada (Asturias) 2012
length(meanBB$tree[!is.na(meanBB$mean)]), # mean bud burst date Bordeaux
length(meanDBB$tree[!is.na(meanDBB$mean)]), # mean duration bud burst Bordeaux
length(data$tree[!is.na(data$POR_SLA)]), # SLA Portugal
length(data$tree[!is.na(data$d13C)])), # delta 13 C Portugal
"Populations" = c(length(unique(data$prov[!is.na(data$POR_htoct12)])), # height Portugal 2012
length(unique(data$prov[!is.na(data$BDX_htnov13)])), # height Bordeaux 2013
length(unique(data$prov[!is.na(data$BDX_htnov18)])), # height Bordeaux 2018
length(unique(data$prov[!is.na(data$AST_htnov12)])), # height Asturias 2012
length(unique(meanBB$prov[!is.na(meanBB$mean)])), # mean bud burst date Bordeaux
length(unique(meanDBB$prov[!is.na(meanDBB$mean)])), # mean duration bud burst Bordeaux
length(unique(data$prov[!is.na(data$POR_SLA)])), # SLA Portugal
length(unique(data$prov[!is.na(data$d13C)]))), # delta 13 C Portugal
"Clones" = c(length(unique(data$clon[!is.na(data$POR_htoct12)])), # height Portugal 2012
length(unique(data$clon[!is.na(data$BDX_htnov13)])), # height Bordeaux 2013
length(unique(data$clon[!is.na(data$BDX_htnov18)])), # height Bordeaux 2018
length(unique(data$clon[!is.na(data$AST_htnov12)])), # height Asturias 2012
length(unique(meanBB$clon[!is.na(meanBB$mean)])), # mean bud burst date Bordeaux
length(unique(meanDBB$clon[!is.na(meanDBB$mean)])), # mean duration bud burst Bordeaux
length(unique(data$clon[!is.na(data$POR_SLA)])), # SLA Portugal
length(unique(data$clon[!is.na(data$d13C)]))), # delta 13 C Portugal
"Blocks" = c(length(unique(data$block[!is.na(data$POR_htoct12)])), # height Portugal 2012
length(unique(data$block[!is.na(data$BDX_htnov13)])), # height Bordeaux 2013
length(unique(data$block[!is.na(data$BDX_htnov18)])), # height Bordeaux 2018
length(unique(data$block[!is.na(data$AST_htnov12)])), # height Asturias 2012
length(unique(meanBB$block[!is.na(meanBB$mean)])), # mean bud burst date Bordeaux
length(unique(meanDBB$block[!is.na(meanDBB$mean)])), # mean duration bud burst Bordeaux
length(unique(data$block[!is.na(data$POR_SLA)])), # SLA Portugal
length(unique(data$block[!is.na(data$d13C)]))) # delta 13 C Portugal
) %>%
kable(caption="Table S1 of the paper",escape = FALSE) %>%
kable_styling(font_size=11,
bootstrap_options = c("striped","hover", "condensed"), full_width = F)| Traits | Common gardens | Dates of measurement | Tree age | Survival | Units | Trees | Populations | Clones | Blocks |
|---|---|---|---|---|---|---|---|---|---|
| Height | Portugal | October 2012 | 20 | 0.66 | mm | 2746 | 33 | 521 | 8 |
| Height | Bordeaux | November 2013 | 25 | 0.97 | mm | 3238 | 33 | 430 | 8 |
| Height | Bordeaux | November 2018 | 85 | 0.96 | mm | 3209 | 33 | 430 | 8 |
| Height | Cabada | November 2012 | 21 | 0.96 | mm | 3973 | 33 | 522 | 8 |
| Mean bud burst date | Bordeaux | 2013, 2014, 2015, 2017 |
|
|
°C-day | 3175 | 33 | 430 | 8 |
| Mean duration of bud burst | Bordeaux | 2014, 2015 2017 |
|
|
°C-day | 3187 | 33 | 430 | 8 |
| Surface Leaf Area | Portugal | NA | NA |
|
m\(^{2}\)/kg | 2642 | 33 | 520 | 8 |
| \(\delta^{13}\)C | Portugal | NA | NA |
|
‰ | 1939 | 33 | 517 | 8 |
tab <- c("MeanBB","MeanDBB") %>%
mclapply(function(x) {
if(x=="MeanBB") data <- meanBB else if(x=="MeanDBB")
data <- meanDBB
data %>%
dplyr::select(prov,mean) %>%
drop_na() %>%
dplyr::rename(value=mean) %>%
group_by(prov) %>%
dplyr::summarise_at("value",list(mean=mean,variance=var)) %>%
dplyr::mutate(trait=x)}) %>%
bind_rows() %>%
pivot_wider(names_from=trait,values_from=c(mean,variance))
tab <- c("POR_htoct12","BDX_htnov13","BDX_htnov18","AST_htnov12","POR_SLA","d13C") %>%
mclapply(function(x)
data %>%
dplyr::select(prov,x) %>%
drop_na() %>%
group_by(prov) %>%
dplyr::summarise_at(all_of(x),list(mean=mean,variance=var)) %>%
mutate(trait=x)) %>%
bind_rows() %>%
pivot_wider(names_from=trait,values_from=c(mean,variance)) %>%
inner_join(tab,by="prov") %>%
dplyr::select(prov,contains("mean_"),contains("var"))
tab %>%
kable(digits=2,caption="Table S2 of the paper",escape = FALSE) %>%
kable_styling(font_size=10,
bootstrap_options = c("striped","hover", "condensed"), full_width = F) %>%
column_spec(1, bold = T)| prov | mean_POR_htoct12 | mean_BDX_htnov13 | mean_BDX_htnov18 | mean_AST_htnov12 | mean_POR_SLA | mean_d13C | mean_MeanBB | mean_MeanDBB | variance_POR_htoct12 | variance_BDX_htnov13 | variance_BDX_htnov18 | variance_AST_htnov12 | variance_POR_SLA | variance_d13C | variance_MeanBB | variance_MeanDBB |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ALT | 525.56 | 760.19 | 4275.38 | 764.43 | 5.08 | -25.94 | 1333.05 | 883.19 | 25044.03 | 25994.08 | 518190.0 | 43958.36 | 0.56 | 1.26 | 10179.89 | 11621.08 |
| ARM | 505.48 | 793.33 | 4450.16 | 818.89 | 5.19 | -26.01 | 1282.95 | 859.95 | 14693.67 | 30154.84 | 416420.9 | 27155.20 | 0.53 | 0.75 | 5787.12 | 10512.04 |
| ARN | 423.44 | 651.87 | 3302.00 | 652.35 | 5.19 | -26.83 | 1309.37 | 861.13 | 20251.08 | 26534.21 | 487704.6 | 21763.14 | 0.96 | 1.68 | 1199.55 | 5285.84 |
| BAY | 394.29 | 618.36 | 3054.03 | 592.79 | 5.18 | -26.49 | 1299.02 | 829.97 | 15606.98 | 24433.97 | 461502.5 | 20475.10 | 0.96 | 1.91 | 5347.63 | 7758.00 |
| BON | 421.33 | 600.43 | 3273.83 | 702.61 | 5.37 | -26.84 | 1197.00 | 752.85 | 15994.80 | 30225.90 | 1159572.0 | 26013.68 | 1.08 | 1.12 | 3843.38 | 7596.92 |
| CAD | 481.58 | 847.76 | 4631.79 | 781.60 | 5.34 | -25.79 | 1342.52 | 898.14 | 32160.59 | 55641.88 | 894272.5 | 42048.76 | 0.85 | 1.11 | 13379.00 | 8728.85 |
| CAR | 405.52 | 505.32 | 2321.30 | 584.58 | 4.81 | -26.74 | 1282.36 | 813.30 | 18268.47 | 18912.40 | 396287.2 | 11029.61 | 0.58 | 1.22 | 9461.82 | 8783.43 |
| CAS | 501.67 | 753.01 | 4202.78 | 769.32 | 5.07 | -25.90 | 1310.38 | 877.46 | 28617.67 | 35004.68 | 554102.0 | 30359.25 | 1.00 | 1.01 | 6072.29 | 12050.36 |
| CEN | 484.82 | 663.51 | 3348.65 | 723.14 | 4.91 | -26.32 | 1272.32 | 849.04 | 25174.51 | 31090.09 | 648606.5 | 26016.07 | 0.58 | 1.36 | 7633.28 | 15793.70 |
| COC | 406.47 | 587.13 | 2735.71 | 588.77 | 5.22 | -26.47 | 1320.11 | 848.06 | 17647.00 | 25181.40 | 573367.0 | 25089.71 | 0.89 | 1.39 | 6714.06 | 7676.71 |
| COM | 516.92 | 809.33 | 3220.71 | 756.13 | 4.86 | -26.14 | 1260.71 | 836.87 | 22918.15 | 35020.23 | 342303.2 | 57311.18 | 0.53 | 0.83 | 4511.64 | 11303.29 |
| CUE | 402.29 | 587.38 | 2793.39 | 582.10 | 5.06 | -26.35 | 1313.72 | 851.46 | 15749.06 | 29948.47 | 745261.0 | 19274.90 | 1.02 | 1.76 | 7290.54 | 8915.74 |
| HOU | 549.58 | 841.95 | 4578.18 | 831.13 | 5.32 | -25.79 | 1287.73 | 832.88 | 30575.88 | 36168.73 | 683021.5 | 37114.18 | 0.80 | 1.09 | 3998.32 | 8832.28 |
| LAM | 443.56 | 706.79 | 3824.82 | 736.38 | 5.37 | -26.25 | 1322.51 | 893.70 | 18614.34 | 28385.84 | 503323.6 | 35455.80 | 0.90 | 1.44 | 4263.94 | 6633.86 |
| LEI | 467.30 | 765.64 | 4156.02 | 743.20 | 5.12 | -26.34 | 1317.84 | 880.28 | 23362.76 | 44161.14 | 799974.2 | 41372.76 | 0.81 | 1.29 | 8737.24 | 11448.55 |
| MIM | 447.76 | 783.87 | 4182.64 | 718.20 | 5.34 | -26.05 | 1283.85 | 832.76 | 22395.23 | 51903.59 | 755436.3 | 33853.99 | 0.91 | 1.94 | 6008.16 | 8888.56 |
| OLB | 445.60 | 700.35 | 3410.89 | 712.97 | 5.24 | -26.43 | 1285.80 | 817.56 | 15336.79 | 23496.30 | 487763.2 | 24782.38 | 0.92 | 1.28 | 5575.52 | 9353.93 |
| OLO | 549.10 | 803.57 | 4390.16 | 828.55 | 5.41 | -25.63 | 1287.50 | 829.00 | 22867.78 | 47499.69 | 534286.6 | 29013.66 | 0.84 | 1.08 | 4316.43 | 9240.09 |
| ORI | 375.58 | 667.40 | 2935.64 | 639.17 | 5.40 | -26.92 | 1239.57 | 797.85 | 15107.03 | 21736.00 | 391840.3 | 25020.48 | 1.10 | 0.94 | 4685.37 | 7988.44 |
| PET | 525.13 | 829.51 | 4471.39 | 781.10 | 5.29 | -25.81 | 1306.26 | 864.40 | 24369.26 | 38187.87 | 665897.4 | 41792.11 | 0.67 | 1.53 | 3723.47 | 10308.61 |
| PIA | 494.60 | 838.62 | 4163.58 | 783.88 | 5.22 | -26.51 | 1201.04 | 793.13 | 28122.80 | 44180.50 | 558897.3 | 35430.62 | 0.84 | 1.39 | 3697.91 | 9221.80 |
| PIE | 435.18 | 630.77 | 3318.63 | 620.29 | 5.43 | -26.26 | 1278.11 | 847.36 | 19269.41 | 23336.65 | 472940.1 | 30191.09 | 1.02 | 1.37 | 3240.16 | 7546.22 |
| PLE | 468.99 | 770.15 | 4044.84 | 683.62 | 5.42 | -25.92 | 1306.42 | 870.85 | 25399.24 | 46364.32 | 868825.2 | 33619.27 | 1.12 | 1.23 | 5312.36 | 7901.84 |
| PUE | 520.26 | 865.10 | 4460.59 | 775.65 | 4.98 | -26.10 | 1353.32 | 902.72 | 33564.79 | 45797.49 | 841781.7 | 37674.17 | 0.72 | 1.11 | 5397.23 | 8249.97 |
| QUA | 458.63 | 706.61 | 3265.79 | 689.08 | 4.92 | -26.74 | 1304.61 | 862.73 | 20764.43 | 25415.59 | 679962.7 | 27766.85 | 0.61 | 1.53 | 5845.11 | 8570.03 |
| SAC | 436.84 | 779.80 | 4317.55 | 699.71 | 5.17 | -25.69 | 1333.98 | 882.42 | 10276.24 | 26602.00 | 665431.4 | 40020.50 | 0.56 | 0.57 | 10387.92 | 11888.99 |
| SAL | 396.90 | 582.90 | 2915.65 | 592.48 | 5.33 | -26.74 | 1253.29 | 802.65 | 20116.52 | 15944.42 | 339030.8 | 19588.04 | 0.95 | 1.01 | 5040.26 | 6804.81 |
| SEG | 453.87 | 819.44 | 4484.53 | 728.92 | 5.12 | -26.06 | 1320.39 | 878.22 | 19458.77 | 47514.06 | 767773.7 | 26366.13 | 0.81 | 1.07 | 6328.79 | 10553.06 |
| SIE | 445.13 | 860.65 | 4266.96 | 755.56 | 5.42 | -26.11 | 1337.77 | 887.32 | 28151.96 | 49975.12 | 431452.8 | 29270.25 | 0.88 | 1.34 | 5703.93 | 7581.65 |
| STJ | 535.06 | 893.84 | 4559.44 | 794.76 | 5.43 | -25.92 | 1296.49 | 838.08 | 23677.59 | 45913.57 | 632962.2 | 37600.18 | 0.81 | 1.60 | 6258.52 | 10697.50 |
| TAM | 339.09 | 570.29 | 2250.45 | 555.13 | 5.76 | -27.82 | 1261.34 | 813.01 | 12214.55 | 16014.62 | 365171.0 | 23907.35 | 0.79 | 0.89 | 3427.87 | 7982.86 |
| VAL | 431.30 | 672.13 | 3393.73 | 656.09 | 5.13 | -26.56 | 1258.81 | 825.76 | 16849.74 | 43523.72 | 860272.1 | 23268.04 | 0.92 | 1.06 | 6513.53 | 10003.81 |
| VER | 503.62 | 813.65 | 4401.61 | 780.73 | 5.46 | -25.75 | 1325.48 | 864.43 | 22141.52 | 44992.37 | 680372.1 | 34422.39 | 0.73 | 1.57 | 4422.45 | 8800.61 |